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It has been proven that in some cases Feng's soft rough set model could provide better approximations than classical rough sets.
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Better approximations than those in Equations 17 and 119 are obtained by making only one of the two assumptions above.
It is further demonstrated, by application of this example, that the MSE represents a better approximation than does the classical linearised shallow water equation (SWE) in the case of extremely small depth.
The refining improvement of (18c) gets more pronounced with the second order approximation process as a second order approximation is certainly a better approximation than the first order approximation.
In general, the continued fraction method could provide a better approximation than others, and has less numerical computations.
Consequently, ( C, 1 ) ( E, 1 ) gives better approximation than the individual methods ( C, 1 ) and ( E, 1 ).
In this section, we try to check whether the new generalization of operator introduced by Deo et al.[2] and given by Equation 2 has better approximation properties than the classical operator given by Equation 1.
While this modeling strategy cannot compare to the precision of ODE models, it provides a much closer approximation than classical or binary reaction-contingency based Boolean modeling.
IP can achieve better convergence than classical gradient-based algorithms.
First, the proposed models give better forecasts than classical SVR when a suitable noise distribution is assumed.
Quantitative methods seem to provide better discrimination than classical gel-based MSP.
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